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     Research Journal of Applied Sciences, Engineering and Technology


A Novel Ship-Bridge Collision Avoidance System Based on Monocular Computer Vision

1Yuanzhou Zheng, 1Weiguo Wu, 1Haixiang Xu, 2Langxiong Gan and 3Jingang Yu
1School of Transportation
2School of Navigation, Wuhan University of Technology, Wuhan 430063, China
3National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, Huazhong University of Science and Technology, Wuhan 430074, China
Research Journal of Applied Sciences, Engineering and Technology  2013  4:647-653
http://dx.doi.org/10.19026/rjaset.6.4176  |  © The Author(s) 2013
Received: September 03, 2012  |  Accepted: October 05, 2012  |  Published: June 20, 2013

Abstract

The study aims to investigate the ship-bridge collision avoidance. A novel system for ship-bridge collision avoidance based on monocular computer vision is proposed in this study. In the new system, the moving ships are firstly captured by the video sequences. Then the detection and tracking of the moving objects have been done to identify the regions in the scene that correspond to the video sequences. Secondly, the quantity description of the dynamic states of the moving objects in the geographical coordinate system, including the location, velocity, orientation, etc, has been calculated based on the monocular vision geometry. Finally, the collision risk is evaluated and consequently the ship manipulation commands are suggested, aiming to avoid the potential collision. Both computer simulation and field experiments have been implemented to validate the proposed system. The analysis results have shown the effectiveness of the proposed system.

Keywords:

Monocular vision, moving object detection and tracking, ship-bridge collision,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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